ABAT targeted by miR-183-5p regulates cell functions in liver cancer

Development of a novel risk signature revealing prognostic and tumor microenvironmental features in breast cancer

This study aimed to devise a breast cancer (BC) risk signature for based on pyrimidine metabolism-related genes (PMRGs) to evaluate its prognostic value and association with drug sensitivity. Transcriptomic and clinical data were retrieved from The Cancer Genome Atlas database and Gene Expression Omnibus repository. Pyrimidine metabolism-associated genes were identified from the Molecular Signatures Database collection. A risk signature was constructed through Cox regression and Lasso regression methods. Further, the relationship between the PMRG-derived risk feature and clinicopathological characteristics, gene expression patterns, somatic mutations, drug susceptibility, and tumor immune microenvironment was thoroughly investigated, culminating in the development of a nomogram. PMRGs displayed differential expression and diverse somatic mutations in BC. Univariate Cox analysis identified 36 genes significantly associated with BC prognosis, leading to the categorization of 2 BC molecular subtypes with discernible differences in prognosis. Using Lasso Cox regression, a risk signature composed of 16 PMRGs was established, wherein high-risk scores were indicative of poor prognosis. The PMRG-derived risk feature was also related to chemotherapy regimens and showed significant correlations with sensitivity to multiple drugs. Furthermore, distinct tumor immune microenvironment properties, gene expression profiles, and somatic mutation patterns were evident across varying risk scores. Ultimately, a nomogram was constructed incorporating the PMRGs-based risk signature alongside stage, and chemotherapy status, demonstrating excellent performance in prognosis prediction. We successfully developed a PMRG-based BC risk signature that effectively combines with clinicopathological attributes for accurate prognosis assessment in BC.

Decoding sunitinib resistance in ccRCC: Metabolic-reprogramming-induced ABAT and GABAergic system shifts

Sunitinib, a primary treatment for clear cell renal cell carcinoma (ccRCC), frequently encounters the challenge of resistance development. Metabolic reprogramming, a characteristic change in ccRCC, is likely linked to this resistance. Our research revealed a notable decrease in the expression of the key metabolic gene ABAT in ccRCC, which contributed to diminished sensitivity to sunitinib. Downregulation of ABAT led to an increase in the intracellular level of gamma-aminobutyric acid (GABA), triggering abnormal activation of the G-protein-coupled receptor GABA-B. This activation resulted in increased transactivation of the tyrosine kinase receptors SYK and LYN, thereby reducing the antitumor and antiangiogenic properties of sunitinib. However, the application of SYK and LYN inhibitors successfully inhibited this effect. The transactivation of SYK and LYN caused resistance to the antiangiogenic effects of sunitinib through the upregulation of PGF protein levels. Furthermore, the combined application of an LYN inhibitor with sunitinib has been shown to enhance therapeutic efficacy.

Navigating the blurred path of mixed neuroimmune signaling

Evolution has created complex mechanisms to sense environmental danger and protect tissues, with the nervous and immune systems playing pivotal roles. These systems work together, coordinating local and systemic reflexes to restore homeostasis in response to tissue injury and infection. By sharing receptors and ligands, they influence the pathogenesis of various diseases. Recently, a less-explored aspect of neuroimmune communication has emerged: the release of neuropeptides from immune cells and cytokines/chemokines from sensory neurons. This article reviews evidence of this unique neuroimmune interplay and its impact on the development of allergy, inflammation, itch, and pain. We highlight the effects of this neuroimmune signaling on vital processes such as host defense, tissue repair, and inflammation resolution, providing avenues for exploration of the underlying mechanisms and therapeutic potential of this signaling.

Prognostic correlation between specialized capillary endothelial cells and lung adenocarcinoma

Background

In-depth analysis of the functional changes occurring in endothelial cells (ECs) involved in capillary formation can help to elucidate the mechanism of tumour vascular growth.

Methods

Appropriate datasets were retrieved from the GEO database to obtain single-cell data on LUAD samples and adjacent normal tissue samples. ECs were selected by an automatic annotation program in R and further subdivided based on reported EC marker genes. Functional changes in different types of capillary ECs were then visualized, and the concrete expression was classified by genetic data in the TCGA. Finally, a prognostic model was constructed to predict immunoinfiltration status, survival and drug therapy effects.

Results

The LUAD data contained in the GSE183219 dataset were suitable for our analysis. After dimensionality reduction analysis and cell annotation, EC general capillary and EC aerocyte subsets as capillary specialized phenotypes showed a series of functional changes in tumour samples, with a total of 108 genes found to undergo functional changes. Use of CellPhoneDB revealed a close interaction of activity between ECs. After integration of TCGA, GSE68465 and GSE11969 datasets, the genes obtained were analysed by cluster analysis and risk model construction, identifying 8 genes. Drug sensitivity, immune cell and molecular differences can be accurately predicted.

Conclusions

EC general capillary and EC aerocyte subsets are recognized capillary ECs in the tumour microenvironment, and the functional changes between them are relevant to the prognosis and treatment of LUAD patients and have the potential to be used in target therapy.

Identification and validation of key miRNAs and a microRNA-mRNA regulatory network associated with liver cancer

MiRNAs play crucial regulatory roles in the growth and development of tumor cells by serving as carriers of post-transcriptional regulatory information derived from genes. Investigating the potential function and clinical significance of miRNA-mediated mRNA regulatory networks in liver cancer can offer novel insights and therapeutic strategies for the treatment of this disease. We identified 300 differentially expressed miRNAs, and five miRNAs were identified to be correlated with overall survival and could be used as an independent prognostic. GO enrichment analysis mainly included carboxylic acid biosynthesis, organic acid biosynthesis, peroxisomal membrane, microsomal membrane, DNA binding, C-acyltransferase activity, etc. KEGG enrichment analysis showed that the pathways of target genes related to liver cancer were mainly focused on butyric acid metabolism and partial amino acid metabolism. Eight of the top 10 HUB genes were associated with prognosis, and the expression of four genes was positively correlated with prognosis, of which ABAT, BHMT, and SHMT1 were target genes of hsa-miR-5003-3p. MiR-5003-3p inhibits ABAT/BHMT/SHMT1 expression, thereby promoting liver cancer development. Overall, our study provides new ideas for the treatment of liver cancer, and these five miRNAs may be independent prognostic biomarkers and therapeutic targets for liver cancer patients. And miR-5003-3p may be a critical factor in the mechanism of liver cancer development.

Hsa_circ_0020134 promotes liver metastasis of colorectal cancer through the miR-183-5p-PFN2-TGF-β/Smad axis

Circular RNAs (circRNAs) are a distinct class of non-coding RNAs that play regulatory roles in the initiation and progression of tumors. With advancements in transcriptome sequencing technology, numerous circRNAs that play significant roles in tumor-related genes have been identified. In this study, we used transcriptome sequencing to analyze the expression levels of circRNAs in normal adjacent tissues, primary colorectal cancer (CRC) tissues, and CRC tissues with liver metastasis. We successfully identified the circRNA hsa_circ_0020134 (circ0020134), which exhibited significantly elevated expression specifically in CRC with liver metastasis. Importantly, high levels of circ0020134 were associated with a poor prognosis among patients. Functional experiments demonstrated that circ0020134 promotes the proliferation and metastasis of CRC cells both in vitro and in vivo. Mechanistically, upregulation of circ0020134 was induced by the transcription factor, PAX5, while miR-183-5p acted as a sponge for circ0020134, leading to partial upregulation of PFN2 mRNA and protein levels, thereby further activating the downstream TGF-β/Smad pathway. Additionally, downregulation of circ0020134 inhibited epithelial-mesenchymal transition (EMT) in CRC cells, which could be reversed by miR-183-5p inhibitor treatment. Collectively, our findings confirm that the circ0020134-miR-183-5p-PFN2-TGF-β/Smad axis induces EMT transformation within tumor cells, promoting CRC proliferation and metastasis, thus highlighting its potential as a therapeutic target for patients with CRC liver metastasis.

GABAergic signaling beyond synapses: an emerging target for cancer therapy

Traditionally, γ-aminobutyric acid (GABA) is best known for its role as a primary inhibitory neurotransmitter reducing neuronal excitability in the mammalian central nervous system (CNS), thereby producing calming effects. However, an emerging body of data now supports a function for GABA beyond neurotransmission as a potent factor regulating cancer cell growth and metastasis, as well as the antitumor immune response, by shaping the tumor microenvironment (TME). Here, we review the current knowledge on GABA's effects on the function of tumor cells, tumor-immune interactions, and the underlying molecular mechanisms. Since altered GABAergic signaling is now recognized as a feature of certain types of solid tumors, we also discuss the potential of repurposing existing GABAergic agents as a new class of anticancer therapy.

A novel risk score model based on gamma-aminobutyric acid signature predicts the survival prognosis of patients with breast cancer

Background

Gamma-aminobutyric acid (GABA) participates in the migration, differentiation, and proliferation of tumor cells. However, the GABA-related risk signature has never been investigated. Hence, we aimed to develop a reliable gene signature based on GABA pathways-related genes (GRGs) to predict the survival prognosis of breast cancer patients.

Methods

GABA-related gene sets were acquired from the MSigDB database, while mRNA gene expression profiles and corresponding clinical data of breast cancer patients were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Univariate Cox regression analysis was used to identify prognostic-associated GRGs. Subsequently, LASSO analysis was applied to establish a risk score model. We also constructed a clinical nomogram to perform the survival evaluation. Besides, ESTIMATE and ssGSEA algorithms were used to assess the immune cell infiltration among the risk score subgroups.

Results

A GRGs-related risk score model was constructed in the TCGA cohort, and validated in the GSE21653 cohort. The risk score was significantly related to the overall survival of breast cancer patients, which could predict the survival prognosis of breast cancer patients independently of other clinical features. Breast cancer patients in the low-risk score group exhibited higher immune cell infiltration levels.

Conclusion

A novel prognostic model containing five GRGs could accurately predict the survival prognosis and immune infiltration of breast cancer patients. Our findings provided a novel insight into investigating the immunoregulation roles of GRGs.

Regulation of Gamma-Aminobutyric Acid Transaminase Expression and Its Clinical Significance in Hepatocellular Carcinoma

Background

Gamma-aminobutyric acid transaminase (ABAT) catalyzes the conversion of gamma-aminobutyric acid (GABA) into succinic semialdehyde. Although some evidence supports a key role of ABAT in the progression of hepatocellular carcinoma (HCC), no systematic analysis is available. Thus, this study aimed to investigate the possible mechanisms related to low ABAT expression and the prognostic value and potential functions of ABAT in HCC.

Methods

We obtained relevant datasets from the Encyclopedia of RNA Interactomes, MethSurv, cBioPortal, TISIDB and The Cancer Genome Atlas and used bioinformatic methods to analyze DNA methylation, copy number variation, gene mutation, and upstream microRNAs (miRNAs) of ABAT, exploring the potential relationship between ABAT expression and the prognosis, glycolysis, and immune infiltration in HCC.

Results

The results indicated that ABAT expression was lower in HCC tumor tissues than in normal tissues or adjacent tissues. Low ABAT expression was related to patient age, T stage classification, pathologic stage, histological grade, and alpha-fetoprotein level of HCC. Kaplan-Meier survival analyses indicated that low ABAT expression was correlated with poor HCC prognosis. ABAT was also verified as an independent risk factor in HCC via Cox multivariate analysis. Gene set enrichment analysis showed enrichment in various signaling pathways. Furthermore, DNA methylation, copy number variation, and gene mutation potentially induced low ABAT expression; miR-135a-5p was a potential upstream miRNA of ABAT. Additionally, ABAT expression was associated with glycolysis-related genes, infiltrated immune cells, immunoinhibitors, and immunostimulators in HCC.

Conclusions

Our study reveals that deficient ABAT expression is correlated with disease progression and poor prognosis in HCC because of its role in tumorigenesis and tumor immunity.

Detecting the critical states during disease development based on temporal network flow entropy

Complex diseases progression can be generally divided into three states, which are normal state, predisease/critical state and disease state. The sudden deterioration of diseases can be viewed as a bifurcation or a critical transition. Therefore, hunting for the tipping point or critical state is of great importance to prevent the disease deterioration. However, it is still a challenging task to detect the critical states of complex diseases with high-dimensional data, especially based on an individual. In this study, we develop a new method based on network fluctuation of molecules, temporal network flow entropy (TNFE) or temporal differential network flow entropy, to detect the critical states of complex diseases on the basis of each individual. By applying this method to a simulated dataset and six real diseases, including respiratory viral infections and tumors with four time-course and two stage-course high-dimensional omics datasets, the critical states before deterioration were detected and their dynamic network biomarkers were identified successfully. The results on the simulated dataset indicate that the TNFE method is robust under different noise strengths, and is also superior to the existing methods on detecting the critical states. Moreover, the analysis on the real datasets demonstrated the effectiveness of TNFE for providing early-warning signals on various diseases. In addition, we also predicted disease deterioration risk and identified drug targets for cancers based on stage-wise data.